University of Groningen Quantification of macromolecular crowding and ionic strength in living cells Liu, Boqun

University of Groningen

Quantification of macromolecular crowding and ionic strength in living cells

Liu, Boqun

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CHAPTER

1

Quantification of

macromolecular crowding

in the intracellular

Abstract

Macromolecular crowding alters the biochemical processes in living cells. Here, we present an overview on the complex composition of the cytoplasm, the effects the crowded interior exert on the macromolecular diffusion and conformational changes of proteins, and the quantification of crowding in cells. The recent developments in crowding sensing will be discussed in detail, including the sensing mechanisms, applications, and (dis)advantages of the available probes.

9

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t In tr oduction

Introduction

The cell is the fundamental unit of all living matter1,2. In the cyto-plasm of a cell, there are many proteins, mRNAs, sugars, ions, and other molecules (Fig. 1). The high density of molecules makes the cytoplasm very crowded and under certain conditions, e.g., osmotic upshift and starvation, the cytosol can change into a glass-like solu-tion3. Hence, the intracellular environment is markedly different from aqueous solutions of proteins and nucleic acids. The terms that are employed to describe the crowded situation in vivo are listed in ta-ble 14. In this chapter, we summarize the (macro)molecules that con-tribute to macromolecular crowding of the cytoplasm, the influence of crowding on certain biochemical processes, and the recent devel-opments on macromolecular crowding determination in living cells.

The composition of the cytoplasm

To have a general perception of the macromolecular crowding of the cytoplasm, it is helpful to know the volume of the cell as well as the type and amount of (macro)molecules in the cytoplasm. Cell volume is not a static value and depends on the cell type, the growth and environmental conditions, and the growth rate of the cell10,11. A typ-ical cell volume varies from 0.3–3 µm3 for a bacterial E. coli cell, to 1000–10,000 µm3 for mammalian Hela cells12,13 (Fig. 2). An alterna-tive way to illustrate the crowdedness of the cytoplasm is to take

E. coli as an example; there are ~1.4 proteins in every 1000 nm3 cubic

Fig. 1. The crowded situation in the cytoplasm of Mycoplasma genitalium. A: Schematic

illustration of M. genitalium. B: Cartoon illustration of an enlarged section of the cytoplasm showing proteins, tRNA, GroEL, and ribosomes. C: A further enlarged section of the cytoplasm which is from an all-atomistic molecular dynamics simulation showing water, ions, metabolites, and proteins. Figure adapted from Yu et al.5.

1

Table 1. Crowding-related terms and methods (taken from ref. 4)

Macromolecular volume fraction Φ:

The volume fraction (Φ) is the volume that is occupied by macromolecules relative to the volume of the compartment (for example, the cytoplasm) and is expressed as volume per volume (v/v) Excluded volume

The volume that is accessible to a tracer molecule is decreased in the presence of crowder mole-cules. The volume excluded is the apparent volume of the crowder molecules, which is given by the distance between the centre of a tracer particle and the centre of the crowding molecule. A selection of theories that are relevant to macromolecular crowding

The Asakura–Oosawa depletion theory6 describes the effect of the attraction of two particles due to the depletion of solutes in between those particles. This local solute depletion induces an os-motic pressure difference within the bulk solution that pushes the particles together. The depletion force occurs in crowded solutions and might be applicable to protein crowding, albeit only on a qualitative level. The depletion force explains, for example, the compression of tracer molecules and the size-dependent sorting of crowder molecules in dense solutions.

The Flory Huggins theory7 describes the thermodynamics of polymer solutions but has been adapted to explain the effects of crowding on intrinsically disordered proteins. Besides excluded volume, this theory contains an interaction parameter to account for the miscibility between disor-dered proteins and crowders.

The scaled-particle theory8 has been adapted to quantify the effects of macromolecular crowding. The theory treats proteins and crowders as hard convex particles that cannot overlap, inducing an entropic cost when placing the tracer molecule in a crowded solution compared with a dilute solu-tion. The entropic cost increases (that is, entropy decreases) with larger overlap volume between the crowder and tracer molecule, and the concentration of the crowder.

Soft interactions, weak chemical interactions

Soft interactions affect both entropy and enthalpy, and include non-covalent interactions, such as electrostatic, hydrogen bonding, van der Waals and hydrophobic interactions. Unlike steric interac-tions, they are caused by the chemical nature of the molecules. Soft interactions can either counter-act or enhance the effects of the excluded volume.

Macromolecular confinement

Confinement refers to the phenomenon of volume exclusion due to a fixed (that is, confining) boundary to a macromolecule9 — for example, the membranes that confine the narrow space of the periplasm. Contrary to macromolecular crowding, the free energy cost of confinement is not neces-sarily minimal when a molecule is in its most compact conformation, and whether the molecule has a complementary shape to the confining boundary becomes a factor in the free energy term. Phase separation

Attractions between macromolecules can lead to macromolecule-enriched and macromolecular- depleted phases, or phases that are enriched in a certain type of macromolecule, such as in the eu-karyotic nucleolus and in germ cell granules (P-granules) of Caenorhabditis elegans. Attraction can be induced by the depletion force (Asakura-Oosawa depletion theory) or chemical interaction (similar to the Flory Huggins theory). The phases need to be in osmotic equilibrium, and thus the presence of osmolytes enhances the possibility of forming phases, as high local concentrations of macromol-ecules need to be osmotically balanced. In the bacterial cell, this may lead to spatial heterogeneity and vectorial chemistry.

Homeocrowding

We use homeocrowding as an acronym of ‘macromolecular crowding homeostasis’; it refers to the ability of the cell to maintain relatively constant levels of macromolecules, similarly to the ability of a system to regulate its internal pH and ion concentration (that is, pH and ion homeostasis or ionic strength homeostasis)

Donnan effect

The Donnan effect (also known as the Gibbs-Donnan effect or Donnan equilibrium) occurs when large particles (for example, anionic proteins) are present on one side of a membrane and create an uneven electrical charge. This will attract small cations, such as K+, the accumulation of which will increase internal osmotic pressure

11

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t In tr oduction

box (10x10x10 nm) in the cytoplasm. The diameter of a protein with 300 residues is about 4.5 nm and that of a protein of 1000 amino acids is about 7 nm14, which corresponds to a volume fraction of ~8% and ~25%, respectively. This means that on average, proteins are separated from each by distances less than their own dimensions.

Proteins

Here, we first discuss the molecules that crowd the cytoplasm. Pro-teins are the most abundant type of macromolecules in the cyto-plasm with an estimated concentration ranging from 0.2 × 106 fL−1 for U2OS cell to 5 × 106 fL−1 for Leptospira interrogans15, and for a single type protein, it can be over its solubility limit16,17. Proteins occupy a relatively large volume in comparison with other molecules such as metabolites and ions. The large amount and volume makes proteins one of the main crowders in the cell.

Proteins are influenced by macromolecular crowding as a result of the excluded volume effect19 (see below). Most of proteins need to fold and assemble into unique three-dimensional structures to fulfill their biological functions20,21. Proteins also oligomerize22−24 and ag-gregate25,26 in vivo. The conformational changes and associations de-pend on the properties of the protein and are affected by excluded volume effects and soft interactions (Table 1). Additionally, proteins themselves and other molecules, including mRNA, DNA and small molecules may associate in larger organizations27−29, which are also Fig. 2. Schematic illustration of the major components of three typical model cells. A bacterial

cell (E. coli), a unicellular eukaryote (the budding yeast S. cerevisiae), and a mammalian cell line (HeLa). Figure adapted from Milo & Phillips18.

1

influenced by excluded volume effects and soft interactions. Thus, proteins are a major crowder in the cytoplasm and they are influ-enced by macromolecular crowding themselves

Genome (DNA)

In prokaryotic cells, genomic DNA is exposed to the cytoplasm, whereas in eukaryotic cells, the DNA is present in the nuclear matrix. Genomic DNA occupies about 1% of the dry weight of E. coli, which is not expected to contribute significantly to macromolecular crowd-ing. Nevertheless, it encodes the proteins27 that crowd the cytoplasm. Additionally, DNA influences viscosity and heterogeneity in the cell due to various factors30. Ribosome complexes (polysomes) are ex-cluded from the nucleus due to the depletion force (Table 1) of DNA, which is a macromolecular crowding effect31. Macromolecular crowd-ing influences the structure and function of DNA by influenccrowd-ing for example the conformation of DNA hairpins32. Additionally, increasing crowding in vitro by the addition of PEG leads to enhancement of DNA replication33 and transcription/translation34.

Ribosome

The ribosome is a complex macromolecule consisting of ribosomal RNA molecules and proteins. Ribosomes occupy 21–37% of the dry weight of E. coli35. The high volume of ribosomes suggests that they would play an important role in macromolecular crowding. Addition-ally, the ribosome functions as a factory for protein synthesis36−38, and the cell regulates ribosome concentration as a function of growth rate and the nutritional contents of the medium39. Hence, the ribosome ma-chinery may directly or indirectly control the macromolecular crowding

in vivo40. Macromolecular crowding affects the assembly of ribosomes.

For example, the presence of a synthetic polymer can increase the for-mation of 70 S ribosomes from 30 S and 50 S ribosomal subunits as a result of excluded volume effects41. Hence, ribosomes influence mac-romolecular crowing by their abundance and activity, and are affected by the macromolecular crowding themselves.

Small molecules

The cell contains a large amount of small molecules. Researchers have quantified more than 100 types of small molecules in exponentially growing E. coli42. Small molecules themselves do not contribute much to the crowding effect. However, they are able to influence macro-molecular crowding by affecting macro-molecular interactions. For example, small ions screen electrostatic interaction and specific molecules, such as ATP which can function as a biological hydrotrope, may influ-ence the macromolecular crowding by keeping proteins soluble and preventing protein aggregate formation43.

13

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t In tr oduction

In brief, the composition of the cytoplasm, which include proteins, rRNA and other macromolecules, results in macromolecular crowding effects. The molecules in the cell influence macromolecular crowding by changing the abundance, assembly and aggregation of macromol-ecules44−49, which influences many biochemical functions.

The effect of macromolecular crowding

Due to the crowded nature of the cytoplasm, most biochemical pro-cesses in vivo, such as lateral diffusion, conformational changes, and aggregation or association of the biomacromolecules, are different from those in dilute aqueous solutions9,50. Here we provide further de-tail on the influence of macromolecular crowding on protein diffusion and conformation, because these phenomena are used for benchmark-ing of the quantification of macromolecular crowdbenchmark-ing with sensors.

The effect of macromolecular crowding on lateral diffusion

Diffusion is the main process for transporting and mixing of compo-nents in prokaryotic cells. The quantitative description of the diffu-sion coefficient (D) is shown by the Stoke-Einstein equation (Eq. 1) in which kB is Boltzmann’s constant. According to the equation, we can see that the diffusion coefficient of macromolecules depends on tem-perature (T), hydrodynamic radius of the particle (Rs), and viscosity (μ) of the medium, which is determined by factors like macromolecular crowding51.

7

In brief, the composition of the cytoplasm, which include proteins, rRNA

and other macromolecules, results in macromolecular crowding effects.

The molecules in the cell influence macromolecular crowding by changing

the abundance, assembly and aggregation of macromolecules

44-49

_{, which}

influences many biochemical functions.

The effect of macromolecular crowding

Due to the crowded nature of the cytoplasm, most biochemical processes

in vivo, such as lateral diffusion, conformational changes, and aggregation

or association of the biomacromolecules, are different from those in dilute

aqueous solutions

9,50

. Here we provide further detail on the influence of

macromolecular crowding on protein diffusion and conformation, because

these phenomena are used for benchmarking of the quantification of

macromolecular crowding with sensors.

The effect of macromolecular crowding on lateral diffusion

Diffusion is the main process for transporting and mixing of components

in prokaryotic cells. The quantitative description of the diffusion

coefficient (D) is shown by the Stoke-Einstein equation (Eq. 1) in which k

B

is Boltzmann's constant. According to the equation, we can see that the

diffusion coefficient of macromolecules depends on temperature (T),

hydrodynamic radius of the particle (R

s

), and viscosity (μ) of the medium,

which is determined by factors like macromolecular crowding

51

_.

𝐷𝐷 =

𝐾𝐾𝐵𝐵𝑇𝑇

6𝜋𝜋𝑅𝑅𝑠𝑠𝜇𝜇

Eq. 1

The macromolecular crowding decreases the mobility of molecules,

which then may become rate-limiting for a given biochemical process. In a

crowded solution, the diffusion of both small and large molecules decreases

but the impact of crowding is more pronounced for large than for small

molecules

52

_{. In vitro, both the diffusion of small (rhodamine green) and}

large molecules (DNA, RNA, proteins, and nanosphare) decrease as a

function of increasing concentration of Ficoll 70 (Fig. 3)

53

_{. The highly}

branched polymeric nature of Ficoll 70 may prevent size selective diffusion

that would be expected from hard-sphere crowders. In vivo, the decrease

of diffusion is more complex, where it is influenced by spatial

Eq. 1 The macromolecular crowding decreases the mobility of mole-cules, which then may become rate-limiting for a given biochemical process. In a crowded solution, the diffusion of both small and large molecules decreases but the impact of crowding is more pronounced for large than for small molecules52. In vitro, both the diffusion of small (rhodamine green) and large molecules (DNA, RNA, proteins, and nanosphare) decrease as a function of increasing concentration of Ficoll 70 (Fig. 3)53. The highly branched polymeric nature of Fi-coll 70 may prevent size selective diffusion that would be expected from hard-sphere crowders. In vivo, the decrease of diffusion is more complex, where it is influenced by spatial heterogeneity and weak interactions. It has been hypothesized that the cytoplasm is spatially organized, which allows the free passage of small molecules while restricting the diffusion of macromolecules52,54.

1

The effect of macromolecular crowding on protein

conformational changes

Most proteins need to fold to be functional. There are several envi-ronmental factors that influence protein folding and conformational changes19,56,57. Macromolecular crowding influences protein folding by the excluded volume effect and soft interactions. The excluded volume effect is dependent on the radius of crowder and the radius of the tracer molecule, i.e. smaller tracers experience less excluded volume effects than bigger ones (Fig. 4). Macromolecular crowding decreases the available volume for the tracer, decreasing the entropy, which raises the free energy of the system. To minimize the decrease in entropy, the available volume is increased by e.g. folding or com-pression of proteins4,8,58. In general, molecular crowding favors more Fig. 3. Diffusion coefficients of different (macro)molecules as a function of Ficoll 70 concentration. A: Diffusion coefficients of rhodamine green as a function of Ficoll 70

concentration shown on linear and log (inset) scales (mean ± SE, 10–20 measurements). B: Diffusion of indicated small solutes, macromolecules, and nanospheres in saline solutions crowded with Ficoll 70. Figures adapt from Dauty et al. 55.

Fig. 4. Effects of macromolecular crowding on protein folding. The folded native state of

a biomolecule (red) is favored compared to the unfolded state (green), because it is more compact and occupies less volume, leaving more volume for the crowders. Figure adapted from Gnutt et al.61.

15

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t In tr oduction

compact forms over extended ones and can thereby shift the equi-librium towards the folded state59. However, some intrinsically disor-dered proteins do not show significant compaction in the presence of macromolecular crowding agents60, which has been ascribed to com-pensation by weak attractive interactions with the crowders.

Techniques for quantification of macromolecular

crowding in living cells

We discuss the techniques for quantification of macromolecular crowding, which either probe the lateral diffusion of fluorescent mol-ecules or sense the conformation fluorescent reporters as a function of crowding strength. Various techniques allow estimation of mac-romolecular crowding by determining the cellular dry weight frac-tion and volume determinafrac-tion4, but they are not considered here because of the limited temporal and spatial resolution as well as the requirement to disrupt the cells. Here, we focus on NMR, FRAP and FCS to measure crowding in the physiological environment. More-over, we will also discuss the newly developed crowding sensors, which are based on FRET or fluorescence anisotropy.

NMR (Nuclear magnetic resonance spectroscopy)

NMR spectroscopy is a powerful tool for the characterization of pro-tein conformations in vivo62,63. NMR can provide the conformations at the atomic level and has a distinct time window which ranges from picoseconds to seconds64. Labeling protein with 19F allows character-izing large proteins up to 100 kDa in living cells65,66. For example, by labeling globular and disordered proteins with 19F, Li et al. observed the site-specific structural and dynamic information of a set of differ-ent proteins in cells, which are influenced by macromolecular crowd-ing, among other effects. The high temporal resolution of NMR en-ables investigation of the protein conformation changes in vivo; thus, probing the macromolecular crowding effect.

Even though there are several advantages, NMR is still limited by several factors; NMR demands a high concentration of protein, which can be achieved by over-expression and microinjection, but both of these two strategies may influence the in vivo macromolec-ular crowding. The high concentration of protein also may lead to inclusion body formation in vivo, which influences the protein con-formations67,68. The need for long acquisition times in a NMR tube can result in a different physiology of the cell compared to regular growth conditions. In short, NMR can be employed for quantification of macromolecular crowding in the living cell, but requires high pro-tein expression of the propro-teins and relatively long acquisition times.

1

FRAP (Fluorescence recovery after photobleaching)

FRAP is a method for quantifying molecular mobility, which is inenced by macromolecular crowding69−71. By measuring the rate of flu-orescence recovery at a previously bleached site, one can estimate the diffusion coefficient of any fluorescently labeled molecule. For example, Konopka et al.54 determined the diffusion coefficient of green fluorescent protein (GFP) within the cytoplasm of E. coli by FRAP and they found that the diffusion of GFP decreases with the os-motic upshift. Van den Bogaart et al.72 determined the diffusion coef-ficients of (macro)molecules of different sizes (from ~0.5 to 600 kDa) in E. coli under normal osmotic conditions and osmotic upshift; they showed the cytoplasm of E. coli appears as a meshwork allowing the free passage of small molecules while restricting the diffusion of big-ger ones. Drawbacks of FRAP are that the spatial resolution is limited to one dimension, the size of the bleached spot, and the fluorescent proteins need to be expressed at relatively high levels. Furthermore, the methodes do not have single molecule resolution, reporting the ensemble average of the diffusion coefficient.

FCS (Fluorescence correlation spectroscopy)

FCS detects the diffusion by tracking the temporal correlations in fluo-rescence intensity fluctuations, which is caused by one or more fluores-cent molecules diffusing in and out of an illuminated excitation volume. It allows measuring protein diffusion at low concentrations without the need of overexpression and provides quantitative data on diffusion coefficients73. By tagging one domain of the C/EBPα with either RFP, mCherry, or Ruby2, Tsekouras et al.74 characterized the diffusion of the proteins in solution and in mouse cells. Within the cell, they looked at diffusion of the labeled proteins in the cytosol and in the nucleus, that is, away from areas of heterochromatin. They found that the complex heterogeneous environment of the cell often shows strong deviations from normal diffusion, which may relate to crowding or confinement. However, FCS works only within a very limited concentration range. If the concentration is too high (typically > 10−8 M), the amount of sig-nal may be too high for the detector which results in inefficient pho-ton counting. If the concentration is too small (typically < 10−13 M), the probability to find a molecule within the detection region becomes extremely small. Moreover, FCS is dependent on the geometry of the detection volume for which the appropriate corrections need to be made. The geometry can be influenced by factors including refractive index of the sample, thickness of the coverslips, and optical satura-tion75, which can alter the measurement of the correlation function. In brief, FCS can be employed to quantify crowding differences through changes in lateral diffusion of a reporter, but the analysis of fluctua-tion data obtained from fluorescent proteins is not trivial.

17

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t In tr oduction

Fluorescence anisotropy

Fluorescence anisotropy or polarization measures the difference in flu-orescence intensities emitted parallel and perpendicular to the polar-ization of the excitation light, which reports on the rotational motion of the entire dye molecule within the excited-state lifetime76,77. Based on fluorescence anisotropy, researchers reported a new strategy that al-lows spatiotemporal visualization of the macromolecular crowding ef-fect in cells78. Briefly, an amine-reactive aggregation-induced emission fluorogen is used to label proteins in the cytoplasm, and the change in protein mobility and local viscosity can be monitored by fluorescence anisotropy and fluorescence lifetime imaging, respectively (Fig. 5).

The crowding measurement depends on the quantification of intra-cellular viscosity, which can be quantified by monitoring the rotation of the phenyl ring. The crowding measurement is also influenced by the labeling process, which includes the concentrations of dye used, and the labeling selectivity. For instance, the dye TPE-Py-NCS can also light up the membrane-bound organelles. Hence, because of the depen-dence on viscosity and staining, this approach has limited usefulness.

FRET (Förster resonance energy transfer)

FRET (Förster resonance energy transfer) is an electrodynamic phe-nomenon that is widely used in biology79−81. Our probes, and later

Fig. 5 Design principle of a crowding sensor based on fluorescence anisotropy. An

amine-reactive dye TPE-Py-NCS labels proteins and probes the macromolecular crowding. The viscosity of the micro-environment can be monitored by determining the rotation of the phenyl ring, which is not affected by crowding, through fluorescence lifetime imaging microscopy (FLIM). The crowding will slow the rotation of the entire protein, which can be quantified with fluorescence anisotropy imaging microscopy (FAIM). Figure adapted from Soleimaninejad et al.78.

1

developed sensors for the quantification of macromolecular crowd-ing in vivo, are based on FRET readout.

BASIC PRINCIPLE OF FRET

The basic mechanism of FRET can be explained with classical physics. FRET occurs between a donor (D) in the excited state and an acceptor Fig. 6. The mechanism of FRET and the crowding sensors. A: The FRET efficiency increases

with the distance decrease between donor and acceptor. When the distance between donor and acceptor is short, the FRET efficiency is high, yielding a high intensity of acceptor. B: The dipole orientation (indicated by arrow) influences the FRET efficiency. When the dipoles are in head-to-tail parallel orientation, there is more energy transfer from donor to acceptor yielding a high intensity of acceptor. C: An example of the FRET efficiency (Eq. 1) varying with distance (R0 = 5.4 nm) D: Schematic drawing of crowding sensor from Boersma et al. A conformationally flexible linker connects a mCerulean3 (cyan fluorescent protein) and a mCitrine (yellow fluorescent protein). Sensor compaction results in a change in FRET efficiency. Figure adapted from Boersma et al.86. E: Schematic drawing of a sensor based PEG (shown in blue)87, labeled at the N- and C-termini, using Atto488 and Atto565. Figure adapted from Gnutt et al.87. F: Schematic drawing of the sensor from Morikawa et al. CFP and YFP1G (A destabilized YFP) were fused with a GGSGGT linker. The fluorescence of YFP1G decreases with high protein concentration, while the fluorescence of cyan fluorescent protein (CFP) is insensitive to protein concentration. Figure adapted from Morikawa et al.88.

19

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t In tr oduction

(A) molecule in the ground state. The donor molecules emit at shorter wavelengths that overlap with the absorption spectrum of the accep-tor. Energy transfer occurs without the appearance of a photon and is the result of long-range dipole-dipole interactions between the do-nor and acceptor82.

The energy transfer efficiency, which is termed FRET efficiency, depends on the donor-acceptor distance and the orientation of the dipoles relative to each other (Fig. 6A and B). When the donor-ac-ceptor distance decreases, more energy is transferred from donor to acceptor yielding a decrease in intensity of the donor and an increase in intensity of the acceptor. (Fig. 6A). When the dipoles are in head-to-tail parallel orientation, there is more energy transfer from donor to acceptor; there is no energy transfer when the dipoles are oriented perpendicular to each other.

The quantitative relationship of FRET efficiency is shown in Eq. 2 and Fig 6C, in which E is the FRET efficiency; r is the radius between donor and acceptor; and R0 the Förster distance of this pair of donor and acceptor, i.e. the distance at which the energy transfer efficiency is 50%. R₀ depends on the fluorescence quantum yield of the donor in the absence of the acceptor (QD), Avogadro’s number (N_A), the dipole orientation factor (κ), the refractive index of the medium (n), and the spectral overlap integral (J). For a chosen FRET pair, the dipole orien-tation factor (κ) is the main factor that influences the R0 and is in the range from 0 to 4; 0 referring to perpendicular orientation and 4 cor-responding to head-to-tail parallel orientation. Generally, for a flexible and freely rotating FRET pair, the orientation factor (κ) equals 2/383.

13

more energy is transferred from donor to acceptor yielding a decrease in

intensity of the donor and an increase in intensity of the acceptor. (Fig.

6A). When the dipoles are in head-to-tail parallel orientation, there is more

energy transfer from donor to acceptor; there is no energy transfer when

the dipoles are oriented perpendicular to each other.

The quantitative relationship of FRET efficiency is shown in Eq. 2 and

Fig 6C, in which E is the FRET efficiency; r is the radius between donor

and acceptor; and R

0

the Förster distance of this pair of donor and acceptor,

i.e. the distance at which the energy transfer efficiency is 50%. R

0

depends

on the fluorescence quantum yield of the donor in the absence of the

acceptor (Q

D

), Avogadro's number (N

A

), the dipole orientation factor (κ),

the refractive index of the medium (n), and the spectral overlap integral

(J). For a chosen FRET pair, the dipole orientation factor (κ) is the main

factor that influences the R

0

and is in the range from 0 to 4; 0 referring to

perpendicular orientation and 4 corresponding to head-to-tail parallel

orientation. Generally, for a flexible and freely rotating FRET pair, the

orientation factor (κ) equals 2/3

83

_.

𝐸𝐸 =

𝑅𝑅06

𝑅𝑅06+𝑟𝑟6

Eq. 2

𝑅𝑅

0

=

_128𝜋𝜋9 ln 105_{𝑁𝑁𝐴𝐴} 𝐾𝐾 2_{𝑄𝑄𝐷𝐷}

𝑛𝑛4

𝐽𝐽 Eq. 3

As shown in Fig. 6A, B and C, FRET causes an intensity change in both

donor and acceptor emission. Hence, by detecting the change, we can

quantify FRET either in fluorescence microscopy or fluorescence

spectroscopy. An alternative way to quantify FRET is to determine the

fluorescence lifetime of donor

84,85

_.

Eq. 2

13

more energy is transferred from donor to acceptor yielding a decrease in

intensity of the donor and an increase in intensity of the acceptor. (Fig.

6A). When the dipoles are in head-to-tail parallel orientation, there is more

energy transfer from donor to acceptor; there is no energy transfer when

the dipoles are oriented perpendicular to each other.

The quantitative relationship of FRET efficiency is shown in Eq. 2 and

Fig 6C, in which E is the FRET efficiency; r is the radius between donor

and acceptor; and R

0

the Förster distance of this pair of donor and acceptor,

i.e. the distance at which the energy transfer efficiency is 50%. R

0

depends

on the fluorescence quantum yield of the donor in the absence of the

acceptor (Q

D

), Avogadro's number (N

A

), the dipole orientation factor (κ),

the refractive index of the medium (n), and the spectral overlap integral

(J). For a chosen FRET pair, the dipole orientation factor (κ) is the main

factor that influences the R

0

and is in the range from 0 to 4; 0 referring to

perpendicular orientation and 4 corresponding to head-to-tail parallel

orientation. Generally, for a flexible and freely rotating FRET pair, the

orientation factor (κ) equals 2/3

83

_.

𝐸𝐸 =

𝑅𝑅06

𝑅𝑅₀6_+𝑟𝑟6

Eq. 2

𝑅𝑅

0

=

_128𝜋𝜋9 ln 105_{𝑁𝑁𝐴𝐴} 𝐾𝐾2_{𝑄𝑄𝐷𝐷}

𝑛𝑛4

𝐽𝐽 Eq. 3

As shown in Fig. 6A, B and C, FRET causes an intensity change in both

donor and acceptor emission. Hence, by detecting the change, we can

quantify FRET either in fluorescence microscopy or fluorescence

spectroscopy. An alternative way to quantify FRET is to determine the

fluorescence lifetime of donor

84,85

_.

Eq. 3

As shown in Fig. 6A, B and C, FRET causes an intensity change in both donor and acceptor emission. Hence, by detecting the change, we can quantify FRET either in fluorescence microscopy or fluores-cence spectroscopy. An alternative way to quantify FRET is to deter-mine the fluorescence lifetime of donor84,85.

MACROMOLECULAR CROWDING SENSORS BASED ON FRET. Due to the advantageous properties of FRET, researchers have been able to develop several sensors for quantification of macromo-lecular crowding (See Table 2 and Fig 6D, E, and F). We designed a genetically- encoded FRET sensor by linking mCerulean3 and mCitrine with a conformational flexible linker86, and applied the sensor in bac-terial and mammalian cells (Fig 6F). The crowding conditions in vivo

1

were equivalent to ~ 19% Ficoll, which corresponds reasonably well with the overall dry weight measurements89. Gruebele et al.90,91 also employed this crowding sensor as a template and varied the FRET pair to AcGFP-mCherry for investigating the folding stability and dynam-ics of proteins in gel as well as cellular crowding in U2OS cells. They found that the crowding change responded linearly to cell-volume.

Simultaneously and independent of us, Gnutt et al.87 demon-strated that a synthetic polymer based FRET sensor also allows prob-ing of macromolecular crowdprob-ing. They determined macromolecular crowding in HeLa cells (Fig. 6E), and observed a very low crowding in HeLa cells. This could be due to compensating forces, e.g. van der Waals and hydrophobic forces between the PEG and crowders. These forces can counteract depletion forces and promote stabiliza-tion of expanded chain conformastabiliza-tions. Later they observed the mac-romolecular crowding effect with a modified version of our sensor92.

Morikawa et al.88 used a variant of YFP, whose fluorescence tensity depends on the surrounding protein concentration, as an in-tracellular protein-crowding sensor (Fig. 6D). However, the crowding sensing mechanism of the sensor from Morikawa et al. is not yet clear (see below), and they can only describe qualitative changes in crowd-ing rather than determine absolute crowdcrowd-ing values.

Although these sensors have been specifically designed for crowd-ing senscrowd-ing, other sensors and macromolecules respond to macro-molecular crowding as well. For example, NKCC1 (functioning as a Na-K-Cl cotransporter)93, ATeam (ATP sensor) and DTeam (control for ATeam)94, PGK (phosphoglycerate kinase)95 are all labeled with FRET pairs and show macromolecular crowding sensitivity, which are due to the excluded volume effect and the relatively large volume of these sensors. Nevertheless, these labeled proteins are also strongly influenced by other interactions or the analyte that they bind. As a result, they are less suitable for the quantification of macromolecular crowding and very useful to determine the concentration of specific ligands.

Table 2. A summary of crowding sensors

Donor1 Acceptor Genetically_Encoded Donor_λ

Ex (nm)

Donor λEm (nm)

Acceptor

λEm (nm) Cell type Result Ref

Fluorescent protein destabilization

CFP YFP1G Yes 880 460–500 520–560 HeLa Not clear 88

Compression

Atto488 Atto565 No 490 497-527 581-679 HeLa No crowding 87 mCerulean3 mCitrine Yes 405 450–505 505–797 E. coli_HEK293 ~19% Ficoll 86,96 AcGFP1 mCherry Yes 470 500-600 600-800 GelU-2 OS

cells 90

,₉₁

21

Q uan tifica tion o f macr omolecular cr ow di ng in the in tr ac ellular en vir onmen t Conclusion

When applying crowding sensors, it is beneficial to understand their mechanism of operation. The mechanism of sensing by the probes of Boersma et al. (Fig. 6F) and the sensor from Gnutt et al. (Fig. 6E) can be explained by the change of distance between donor and acceptor as shown in Figure 6A. The mechanism of sensing by the probe of Mori-kawa et al. (Fig. 6D) is less clear but may occur through destabilization of the folding of the fluorescent protein under condition of high crowding.

The sensors from Boersma et al. are mostly sensitive to the exclud-ed-volume effect and less influenced by other forces. Additionally, as the sensors are genetically encoded, they do not require perturbation of the living cell other than effects caused by their expression. Moreover, the genetically encoded FRET sensors can be widely applied in different types of cells. One disadvantage of these sensors is that the fluorescent proteins are less photostable than the synthetic fluorophores used by Gnutt et al. Other points that are inherent to genetically encoded sen-sors will be addressed in this thesis: The maturation of fluorescent pro-teins, incomplete sensor synthesis and sensor degradation (Chapter 3), and the pH sensitivity of the fluorescent proteins (Chapter 4).

Conclusion

The high density of macromolecules causes the crowded nature of cytoplasm. The quantification of macromolecular crowding under varying conditions is important for understanding the physicochemi-cal homeostasis of the cell, and consequently an array of techniques that sense crowding has been developed.

Thesis outline

In this thesis, I shed light on the mechanism of crowding sensing and the development of FRET-based crowding probes; I use these sen-sors to determine the significance of macromolecular crowding in bacterial cells. We show that 1) the effect of macromolecular crowd-ing on the sensor in the livcrowd-ing cells displays polymer-type behavior and its compression scales with the biopolymer volume fraction and sensor size, and depends in vivo on its structure (Chapter 2); 2) the maturation of fluorescent proteins can influence the ratiometric FRET, which can be minimized by constitutive expression of the sen-sor (Chapter 3); and 3) by perturbing the crowding by hyperosmotic stress and leaving the cells to adapt, I show that macromolecular crowding remains at a lower level compared to unstressed cells upon adaptation (chapter 4). Additionally, we present the first sensors to determine the ionic strength in living cells. The ionic strength sensors

1

allow observation of spatiotemporal changes in the ionic strength on the single-cell level in mammalian cells.

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